Electricity Consumption Forecasting Project

Developed a machine learning model to predict electricity demand using historical consumption data. The project involved data cleaning, feature engineering, and exploratory analysis. Multiple models such as Random Forest, LightGBM, and XGBoost were trained and compared, achieving accurate short-term forecasting results. This project demonstrates strong skills in data preprocessing, regression modeling, and model evaluation.This project focused on analyzing and forecasting electricity consumption patterns using a real-world dataset from 2012. The data included daily load measurements for multiple clients, which required extensive preprocessing and time-series feature engineering. Machine learning models such as Random Forest, LightGBM, and XGBoost were applied to predict short-term electricity demand, providing insights into consumption behavior and improving forecasting accuracy.This project analyzed electricity usage data from 2013 to identify consumption patterns and forecast future demand. After data preprocessing and feature extraction, machine learning models including Random Forest, LightGBM, and XGBoost were trained and evaluated. The results provided accurate short-term demand predictions, supporting better planning and resource management.In this project, electricity consumption data from 2014 was used to study demand behavior and forecast future usage. The dataset was preprocessed and enriched with time-based features, followed by training and comparing machine learning models such as Random Forest, LightGBM, and XGBoost. The analysis delivered accurate forecasts that highlight seasonal consumption trends and support efficient energy planning.This project analyzed four years of electricity consumption data (2012–2015) to uncover usage patterns and improve demand forecasting. The dataset included daily and seasonal variations across multiple clients, requiring extensive preprocessing and time-series feature engineering. Machine learning models such as Random Forest, LightGBM, and XGBoost were trained and compared to deliver accurate short-term forecasts. The results revealed clear seasonal trends, growth in overall consumption, and provided insights that can support energy efficiency and resource planning.